Non-experimental rapid identification of lower respiratory tract infections in patients with chronic obstructive pulmonary disease using multi-label learning

Abstract

Background and Objective

Microbiological culture is a standard diagnostic test that takes a long time to identify lower respiratory tract infections (LRTI) in patients with chronic obstructive pulmonary disease (COPD). This study entailed the development of an interactive decision-support system using multi-label machine learning. It is designed to assist clinical medical staff in the rapid and simultaneous diagnosis of various infections in these patients.

Methods

Clinical health record data were collected from inpatients with COPD suspected of having a LRTI. Two major categories of multi-label learning frameworks were integrated with various machine learning algorithms to create 23 predictive models to identify four categories of infection: fungal, gram-negative bacterial, gram-positive bacterial, and multidrug-resistant organism infections. The predictive power of the individual models was tested. Subsequently, the model with the highest comprehensive performance was selected and integrated with SHAP technology to construct a decision support system.

Results

Three-thousand-eight-hundred-one subjects participated in this study. LP-RF recorded the highest overall performance, with a Hamming loss of 0.158 (95 %CI: 0.157–0.159) and a samples-precision of 0.894 (95 %CI: 0.891–0.896). The developed diagnostic decision support system generates predicted probability output for each infection category in a specific patient and displays the interpreted output results.

Conclusion

The developed multi-label decision support system enables effective prediction of four categories of infections in patients with a history of COPD, and has the potential to curb the overuse of antimicrobial drugs. This system is highly explainable and interactive, providing real-time support in the simultaneous diagnosis of multiple infection categories.